Prediction of USD/JPY Exchange Rate Time Series Directional Status by KNN with Dynamic Time Warping AS Distance Function
作者： ArashNegahdari Kia , Dr Samanharatizadeh
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摘要: Exchange rate prediction is a challenging topic in the recent decade. Various studies have been done to improve regarding accuracy terms of level error and directional status error. The aim this paper introduce methodology that uses KNN (K-nearest neighbors) DTW (dynamic time warping) fluctuation better evaluation parameters literature financial market forecasting, comparing other researches. study with USD/JPY(United States Dollar/Japanese Yen) exchange series results show improvement direction series. USD/JPY rates are gathered from 1971 2012 partitioned into 30 element segments monthly cyclic behavior Then two different set these divided 7:3 ratio used find out 3 nearest neighbors as similarity function. By chosen function introduced also research, last predicted result then compared prediction.
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